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ORCIDVincenzo Stefano D'Amato
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2020 – today
- 2024
[j3]Danilo Franco
, Vincenzo Stefano D'Amato, Luca Pasa, Nicolò Navarin, Luca Oneto:
Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing. Neurocomputing 563: 126948 (2024)- 2023
- [b1]Vincenzo Stefano D'Amato:
Deep Multi Temporal Scale Networks for Human Motion Analysis. University of Genoa, Italy, 2023 - [j2]Temitayo A. Olugbade, Marta Bienkiewicz, Giulia Barbareschi, Vincenzo Stefano D'Amato, Luca Oneto, Antonio Camurri, Catherine Holloway, Mårten Björkman, Peter E. Keller, Martin Clayton, Amanda C. de C. Williams, Nicolas Gold, Cristina Becchio, Benoît G. Bardy, Nadia Bianchi-Berthouze:
Human Movement Datasets: An Interdisciplinary Scoping Review. ACM Comput. Surv. 55(6): 126:1-126:29 (2023) - 2022
- [c6]Marco Demutti, Vincenzo Stefano D'Amato, Carmine Tommaso Recchiuto, Luca Oneto, Antonio Sgorbissa:
A Cloud Architecture for Emotion Recognition Based on the Appraisal Theory (short paper). AIRO@AI*IA 2022: 19-24 - [c5]Vincenzo Stefano D'Amato, Luca Oneto, Antonio Camurri, Davide Anguita:
The Importance of Multiple Temporal Scales in Motion Recognition: when Shallow Model can Support Deep Multi Scale Models. IJCNN 2022: 1-10 - [c4]Vincenzo Stefano D'Amato, Luca Oneto, Antonio Camurri, Davide Anguita, Zinat Zarandi, Luciano Fadiga, Alessandro D'Ausilio, Thierry Pozzo:
The Importance of Multiple Temporal Scales in Motion Recognition: from Shallow to Deep Multi Scale Models. IJCNN 2022: 1-9 - [c3]Marco Demutti, Vincenzo Stefano D'Amato, Carmine Recchiuto, Luca Oneto, Antonio Sgorbissa:
Assessing Emotions in Human-Robot Interaction Based on the Appraisal Theory. RO-MAN 2022: 1435-1442 - 2021
- [c2]Vincenzo Stefano D'Amato, Luca Oneto, Antonio Camurri, Davide Anguita:
Keep it Simple: Handcrafting Feature and Tuning Random Forests and XGBoost to face the Affective Movement Recognition Challenge 2021. ACII (Workshops and Demos) 2021: 1-7 - [c1]Vincenzo Stefano D'Amato, Erica Volta, Luca Oneto, Gualtiero Volpe, Antonio Camurri, Davide Anguita:
Accuracy and Intrusiveness in Data-Driven Violin Players Skill Levels Prediction: MOCAP Against MYO Against KINECT. IWANN (2) 2021: 367-379 - 2020
- [j1]Vincenzo Stefano D'Amato, Erica Volta, Luca Oneto, Gualtiero Volpe, Antonio Camurri, Davide Anguita:
Understanding Violin Players' Skill Level Based on Motion Capture: a Data-Driven Perspective. Cogn. Comput. 12(6): 1356-1369 (2020)
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